Abstract
user-generated content volumes and class imbalances hinder accurate multiclass predictions and model interpretability.
This study introduces a novel explainable ensemble learning framework for multiclass SA (positive,
neutral, negative) across three Amazon product domains: appliances, groceries, and clothing. The framework
integrates diverse supervised classifiers in a stacking ensemble, with SHapley Additive exPlanations (SHAP)
innovatively employed not only to elucidate feature contributions but also to rank and interpret the individual
impacts of base classifiers on ensemble predictions, a pioneering application in domain-specific SA, as it enables
global insights into model dynamics and base model selection, addressing gaps in prior studies that relied on
local explanations like LIME (Local Interpretable Model-agnostic Explanations). Evaluated using imbalancesensitive
metrics (weighted/macro F1-score, Matthews Correlation Coefficient, Cohen’s Kappa, Geometric
Mean), the ensemble surpasses individual classifiers and demonstrates higher macro F1 and G-Mean than the
transformer-based ALBERT model, while ALBERT excels in weighted F1, MCC, and Cohen's Kappa. Extra Trees
notably excelled in the G-Mean for minority classes. SHAP analysis uncovers domain-specific drivers and base
model roles, enhancing transparency. The results underscore the framework’s efficacy in delivering robust
performance and actionable insights for trust modelling, automated analytics, and personalized recommendations.
This work lays the groundwork for extensions to low-resource domains, multimodal data, and finer rating
scales, advancing interpretable SA in e-commerce.